The effect of blocking artifacts (such blocking artifacts are also referred to herein as “blockiness”) is a significant factor in assessing the perceptual quality of digital images and digital image sequences, particularly when the digital images and digital image sequences are encoded or compressed to achieve reduced bit rates. For example, in digital still images compressed according to the Joint Photographic Experts Group (JPEG) standards, and in digital image sequences compressed according to the Moving Picture Experts Group (MPEG) standards or the Telecommunication Standardization Sector of the International Telecommunication Union (ITU-T) H.26n standards, frames of digital images are typically divided into a number of blocks of pixels (e.g., 8×8 pixel blocks), and the blocks are subjected to quantization processing, which generally follows the block discrete cosine transform (BDCT) and truncates the high frequency discrete cosine transform (DCT) coefficients. When the encoded digital image signal is later recovered (i.e., decoded or decompressed), blocking artifacts may be produced, causing the boundaries between adjacent blocks (such boundaries are also referred to herein as “block edges”) to appear as discontinuities in the decoded digital image signal.
One known technique for measuring blockiness in digital images detects vertical and horizontal blocking artifacts from the contrast characteristics between local and average gradients for the respective digital images. In accordance with this technique, vertical blocking artifacts can be detected by deriving an image pixel array from a decoded digital image signal, generating a normalized horizontal gradient for the digital image by taking the ratio of the absolute local gradient and the average gradient calculated over adjacent columns of the image pixel array, and accumulating the results over all of the rows of the image pixel array. A histogram analysis of the accumulated signal can then be employed to extract the block size and offsets. The accumulated signal is averaged over the block edge locations and intermediate locations, and a measure of the vertical blockiness in the digital image is obtained by taking the ratio of the average values of the accumulated signal at the block edge locations and at the intermediate locations. A measure of the horizontal blockiness in the digital image can also be obtained in an analogous fashion. This technique for measuring blockiness in digital images has drawbacks, however, such as that it requires the locations of the block edges to be precisely determined.
Another known technique for measuring blockiness in digital images represents a decoded digital image signal as a digital image without blockiness that is interfered with a so-called “ideal blocky image signal”. In accordance with this technique, a measure of blockiness in the decoded digital image signal is obtained by detecting the ideal blocky image signal, estimating the power spectrum of the ideal blocky image signal, and obtaining the blockiness measurement from an analysis of the peaks in the power spectrum curve. This technique for measuring blockiness in digital images also has drawbacks, however, due to its high computational complexity.
Still another known technique can be used to perform frequency domain measurements of blockiness in digital images. In accordance with this technique, luminance discontinuities are extracted from a digital image using a Sobel operator, which is a conventional image processing routine for performing edge extraction. After segmenting a gradient image into small blocks and applying a 2-dimensional (2-D) discrete Fourier transform (DFT) to the small blocks, blockiness in the digital image is translated into harmonics whose amplitude and phase provide information for quantifying the blockiness. The amplitude of the harmonics is proportional to the degree of blockiness, while the phase of the harmonics can be used to verify that the harmonics are not caused by contextual details in the digital image. The blockiness in the digital image can be detected by examining both the amplitude information and the phase information of the harmonics. This technique for performing frequency domain measurements of blockiness in digital images also has drawbacks, however, due to its computational complexity and the need to determine the precise locations of the block edges.
It would therefore be desirable to have systems and methods of measuring blocking artifacts in digital images and digital image sequences that avoid one or more of the drawbacks of known techniques for measuring blockiness in digital images and digital image sequences.